Effect of Socioeconomic Status on Inpatient Mortality and Use of Postacute Care After Subarachnoid Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Studies in the United States and Canada have demonstrated socioeconomic gradients in outcomes of acute life-threatening cardiovascular and cerebrovascular diseases. The extent to which these findings are applicable to subarachnoid hemorrhage is uncertain. This study investigated socioeconomic status-related differences in risk of inpatient mortality and use of institutional postacute care after subarachnoid hemorrhage in the United States and Canada. METHODS: Subarachnoid hemorrhage patient records in the US Nationwide Inpatient Sample database (2005-2010) and the Canadian Discharge Abstract Database (2004-2010) were analyzed separately, and summative results were compared. Both databases are nationally representative and contain relevant sociodemographic, diagnostic, procedural, and administrative information. We determined socioeconomic status on the basis of estimated median household income of residents for patient's ZIP or postal code. Multinomial logistic regression models were fitted with adjustment for relevant confounding covariates. RESULTS: The cohort consisted of 31,631 US patients and 16,531 Canadian patients. Mean age (58 years) and crude inpatient mortality rates (22%) were similar in both countries. A significant income-mortality association was observed among US patients (odds ratio, 0.77; 95% CI, 0.65-0.93), which was absent among Canadian patients (odds ratio, 0.97; 95% CI, 0.85-1.12). Neighborhood income status was not significantly associated with use of postacute care in the 2 countries. CONCLUSIONS: Socioeconomic status is associated with subarachnoid hemorrhage inpatient mortality risk in the United States, but not in Canada, although it does not influence the pattern of use of institutional care among survivors in both countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".